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The growth of photochemistry and high throughput experimentation in well plates and flow drives interest in photochemical platforms that provide spatially uniform irradiation of reactions. Here, we present a design of a versatile, uniform light platform for photochemistry to enable increased performance and reproducibility for high throughput experimentation in shallow well plates, in-plane flow reactors, and droplets. The design of the platform is driven by the development of an open-source ray tracing light simulation package. Radiometry provides experimental validation of the system's irradiance and irradiance uniformity. The usefulness of the approach is demonstrated by application to the photoinduced electron transfer–reversible addition–fragmentation chain transfer polymerization of methyl acrylate.
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LED-based Solar Ring Light Simulator on a Measurescope
Luminance and irradiance uniform ring-light is essential for image processing and machine vision for measure scopes. A curved lens is designed for irradiance uniformity and five types of wavelengths of LEDs are used to fulfill luminance uniformity.
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- Award ID(s):
- 1916866
- PAR ID:
- 10265900
- Date Published:
- Journal Name:
- Adaptive Optics: Analysis, Methods & Systems
- Page Range / eLocation ID:
- JW2A.2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Lu et al. (2025) proved that HDRI camera sensors from different viewpoints can capture consistent and transferable luminance patterns in daylit spaces through Conditional Generative Adversarial Networks (CGANs). Building on that, this paper validates that non-intrusive luminance monitoring can be used to evaluate daylighting preferences, using collected experimental datasets with human subjects at different seating locations in a real open-plan office. To apply paired comparisons for effective learning, subjects compared successive pairs of different visual conditions and indicated their visual preferences through online surveys. Meanwhile, ten small, low-cost, and calibrated cameras captured luminance maps from both the field of view (FOV) of each occupant and non-intrusive viewpoints (on computer monitors, luminaire/ceiling and desk) under various sky conditions and interior luminance distributions. Convolutional Neural Network (CNN) models were developed and trained on luminance similarity index maps (generated from pixel-wise comparisons between successive luminance maps captured from FOV and non-intrusive cameras separately), to classify each subject’s daylight visual preferences. The results showed that the models trained on luminance distributions measured by monitor-mounted and ceiling-mounted cameras produced preference predictions consistent with those derived from FOV cameras, and can reliably learn visual preferences (83-94% accuracy) in all cases except for locations furthest from the windows. Overall, this study is the first to demonstrate that daylight preferences can be learned non-invasively by employing the full potential of HDRI and deep learning techniques, marking a significant milestone toward practical, AI-assisted, human-centered daylighting operation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1364/3D.2020.JW2A.2
